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Added SoftMaxRegOld.
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@ -60,6 +60,7 @@ sources = [
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"mlpp/outlier_finder/outlier_finder_old.cpp",
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"mlpp/probit_reg/probit_reg_old.cpp",
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"mlpp/svc/svc_old.cpp",
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"mlpp/softmax_reg/softmax_reg_old.cpp",
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"test/mlpp_tests.cpp",
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]
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192
mlpp/softmax_reg/softmax_reg_old.cpp
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192
mlpp/softmax_reg/softmax_reg_old.cpp
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//
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// SoftmaxReg.cpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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#include "softmax_reg_old.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include <iostream>
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#include <random>
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MLPPSoftmaxRegOld::MLPPSoftmaxRegOld(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, std::string reg, real_t lambda, real_t alpha) :
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inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), n_class(outputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
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y_hat.resize(n);
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weights = MLPPUtilities::weightInitialization(k, n_class);
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bias = MLPPUtilities::biasInitialization(n_class);
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}
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std::vector<real_t> MLPPSoftmaxRegOld::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxRegOld::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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}
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void MLPPSoftmaxRegOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, outputSet);
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//Calculating the weight gradients
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std::vector<std::vector<real_t>> w_gradient = alg.matmult(alg.transpose(inputSet), error);
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//Weight updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, w_gradient));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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// Calculating the bias gradients
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//real_t b_gradient = alg.sum_elements(error);
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// Bias Updation
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bias = alg.subtractMatrixRows(bias, alg.scalarMultiply(learning_rate, error));
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forwardPass();
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// UI PORTION
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPSoftmaxRegOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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while (true) {
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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real_t outputIndex = distribution(generator);
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std::vector<real_t> y_hat = Evaluate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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// Calculating the weight gradients
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std::vector<std::vector<real_t>> w_gradient = alg.outerProduct(inputSet[outputIndex], alg.subtraction(y_hat, outputSet[outputIndex]));
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// Weight Updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, w_gradient));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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// Calculating the bias gradients
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std::vector<real_t> b_gradient = alg.subtraction(y_hat, outputSet[outputIndex]);
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// Bias updation
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bias = alg.subtraction(bias, alg.scalarMultiply(learning_rate, b_gradient));
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y_hat = Evaluate({ inputSet[outputIndex] });
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
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MLPPUtilities::UI(weights, bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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}
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void MLPPSoftmaxRegOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight gradients
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std::vector<std::vector<real_t>> w_gradient = alg.matmult(alg.transpose(inputMiniBatches[i]), error);
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//Weight updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, w_gradient));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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// Calculating the bias gradients
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bias = alg.subtractMatrixRows(bias, alg.scalarMultiply(learning_rate, error));
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y_hat = Evaluate(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(weights, bias);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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}
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real_t MLPPSoftmaxRegOld::score() {
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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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}
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void MLPPSoftmaxRegOld::save(std::string fileName) {
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MLPPUtilities util;
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util.saveParameters(fileName, weights, bias);
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}
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real_t MLPPSoftmaxRegOld::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
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MLPPReg regularization;
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class MLPPCost cost;
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return cost.CrossEntropy(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
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}
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std::vector<real_t> MLPPSoftmaxRegOld::Evaluate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.softmax(alg.addition(bias, alg.mat_vec_mult(alg.transpose(weights), x)));
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxRegOld::Evaluate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.softmax(alg.mat_vec_add(alg.matmult(X, weights), bias));
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}
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// softmax ( wTx + b )
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void MLPPSoftmaxRegOld::forwardPass() {
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MLPPLinAlg alg;
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MLPPActivation avn;
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y_hat = avn.softmax(alg.mat_vec_add(alg.matmult(inputSet, weights), bias));
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}
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50
mlpp/softmax_reg/softmax_reg_old.h
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50
mlpp/softmax_reg/softmax_reg_old.h
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#ifndef MLPP_SOFTMAX_REG_OLD_H
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#define MLPP_SOFTMAX_REG_OLD_H
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//
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// SoftmaxReg.hpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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#include "core/math/math_defs.h"
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#include <string>
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#include <vector>
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class MLPPSoftmaxRegOld {
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public:
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MLPPSoftmaxRegOld(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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std::vector<real_t> modelTest(std::vector<real_t> x);
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std::vector<std::vector<real_t>> modelSetTest(std::vector<std::vector<real_t>> X);
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
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void SGD(real_t learning_rate, int max_epoch, bool UI = false);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
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real_t score();
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void save(std::string fileName);
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private:
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real_t Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<std::vector<real_t>> Evaluate(std::vector<std::vector<real_t>> X);
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std::vector<real_t> Evaluate(std::vector<real_t> x);
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void forwardPass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<std::vector<real_t>> outputSet;
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std::vector<std::vector<real_t>> y_hat;
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std::vector<std::vector<real_t>> weights;
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std::vector<real_t> bias;
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int n;
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int k;
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int n_class;
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// Regularization Params
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std::string reg;
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real_t lambda;
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real_t alpha; /* This is the controlling param for Elastic Net*/
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};
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#endif /* SoftmaxReg_hpp */
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@ -51,6 +51,7 @@
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#include "../mlpp/outlier_finder/outlier_finder_old.h"
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#include "../mlpp/pca/pca_old.h"
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#include "../mlpp/probit_reg/probit_reg_old.h"
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#include "../mlpp/softmax_reg/softmax_reg_old.h"
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#include "../mlpp/svc/svc_old.h"
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#include "../mlpp/uni_lin_reg/uni_lin_reg_old.h"
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#include "../mlpp/wgan/wgan_old.h"
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@ -399,10 +400,10 @@ void MLPPTests::test_softmax_regression(bool ui) {
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// SOFTMAX REGRESSION
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Ref<MLPPDataComplex> dt = data.load_iris(_iris_data_path);
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MLPPSoftmaxReg model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
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model.SGD(0.1, 10000, ui);
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alg.printMatrix(model.modelSetTest(dt->get_input()->to_std_vector()));
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std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
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MLPPSoftmaxRegOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
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model_old.SGD(0.1, 10000, ui);
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alg.printMatrix(model_old.modelSetTest(dt->get_input()->to_std_vector()));
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std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
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}
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void MLPPTests::test_support_vector_classification(bool ui) {
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//MLPPStat stat;
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